Abstract
In distance education systems, the ranked list of resources is very important for learners and teachers to find useful resources effectively. Apart from user’s interest, the knowledge point of subject is a crucial factor for education system, especially for primary and secondary school oriented distance education. Much of the previous work are models based on recommender systems, however, these models considered only user’s interest, ignoring the crucial impact of subject knowledge. In order to improve the performance of recommender systems, we considered both the subject knowledge and user’s interest. To get this target, Latent Knowledge Model (LKM) is adopted. LKM is a knowledge-based and teaching task-oriented model. It enables subject knowledge resources through knowledge tree extended search strategy, and gets personalized resources through user feature mining strategy. LKM is realized on real data sets which are obtained from a popular distance education teaching platform. Recall and precision rate are used to evaluate the performance of our proposed method for resources recommendation tasks. Experimental results show that the LKM captures subject knowledge and personal preferences for resources selection, which yields significant improvement in recommendation accuracy.
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Notes
- 1.
The education resources datasets can be collected from: http://www.910edu.com/reslibsrv/web/index/index.do.
References
Yunus Y, Salim J (2008) Framework for the evaluation of e-learning in Malaysian Public Sector from the pedagogical perspective. In: International symposium in information technology, pp 1–8
Basu C, Hirsh H, Cohen W (1998) Recommendation as classification: using social and content-based information in recommendation. In: Proceedings of the fifteenth national conference on artificial intelligence, pp 714–720
Pan P, Wang C, Horng G, Cheng S (2010) The development of an ontology-based adaptive personalized recommender system. In: 2010 international conference on electronics and information engineering, pp V176–V180
Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749
Drachsler H, Hummel H, Koper R (2007) Recommendations for learners are different: applying memory-based recommender system techniques to lifelong learning. SIRTEL workshop at the EC-TEL 2007 Conference
Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adapt Interact 12:331–370
Saman S, Seyed Yashar B et al (2011) Review of personalized recommendation techniques for learners in e-learning systems. In: 2011 international conference on semantic technology and information retrieval, pp 277–281
Isabela G, Daniel L et al (2009) Quality ontology for recommendation in an adaptive educational system. In: 2009 international conference on intelligent networking and collaborative systems, pp 329–335
Liu X, Liu N, Du H (2011) Research on personalized service of basic data resources integration platform based on ontology. In: 2011 international symposium on it in medicine and education (ITME), pp 299–304
Acknowledgments
We thank Dayong Wen and the subject experts for their helpful comments and suggestions about extracting subject knowledge nodes from the special subject.
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Zhao, M., Ni, W., Zhang, H., Lin, Z., Yang, Y. (2014). A Knowledge-Based Teaching Resources Recommend Model for Primary and Secondary School Oriented Distance-Education Teaching Platform. In: Li, S., Jin, Q., Jiang, X., Park, J. (eds) Frontier and Future Development of Information Technology in Medicine and Education. Lecture Notes in Electrical Engineering, vol 269. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7618-0_50
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DOI: https://doi.org/10.1007/978-94-007-7618-0_50
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